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PurposeThis paper aims to examine the relationship of supply chain disruption risk drivers to supply chain performance and firm performance outcomes.Design/methodology/approachFour disruption risk drivers for a supply chain are identified, namely, demand disruption risk, supply disruption risk, process disruption risk and environment disruption risk. A cross-sectional survey was developed and data was collected from 315 Chinese firms to determine the relationship of supply chain disruption risks to supply chain performance and firm performance.FindingsThe empirical findings show that supply disruption risks and process disruption risks have a significant impact on supply chain performance. In addition, this paper shows that supply disruptions, demand disruptions and process disruptions are significantly related to firm performance. This paper shows that supply chain disruption risks have different effects on supply chain performance and firm performance. Managers should be aware that disruption risk drivers can have an impact on firm performance that is different from their impact on supply chain performance. An important finding of the study is that the magnitude of the impact of disruption risks on supply chain performance is greater on the upstream side of the supply chain than on the downstream side of the supply chain.Originality/valueThis is one of the early studies to examine the effect of supply chain disruption risk drivers on both firm performance and supply chain performance. An important finding of the study is that the magnitude of the impact of disruption risks on supply chain performance is greater on the upstream side of the supply chain than on the downstream side of the supply chain.

Purpose The purpose of this paper is to develop a model for the production planning decision of a dairy plant in a multi-product setting under supply disruption risk and demand uncertainty while determining the optimal product-mix and material planning requirement. Design/methodology/approach A mixed-integer nonlinear programming model is proposed to determine the optimal product-mix that maximizes the expected profit of a dairy. The data are collected through visits to the dairy site, conducting brainstorming sessions with the plant manager and marketing head at the corporate office. Disruption data are collected from the India Meteorological Department, Odisha. Findings From the analysis, it is recommended that the dairy should not produce curd during the planning period. Moreover, turnover from toned, double toned and baby food is maximum than that of the curd and these products are produced in the planning period. The expected profit increases from its present value when an optimal product-mix is followed. Sensitivity analysis is performed to analyze the effect of demand uncertainty, supply disruption and production quota. The expected profit decreases as the supply failure probability increases. Research limitations/implications The model is implemented in a dairy plant under Orissa State Cooperative Milk Producers Federation, Odisha, India. The proposed methodology has not been validated, theoretically. The concerned dairy is based on the Indian context, but the authors believe that the study is highly relevant to other dairies as well. Practical implications This study provides a methodology for dairy plant managers to plan production effectively under supply disruption risk with demand uncertainty. It also suggests material requirement planning at different factories of the dairy plant. Originality/value This paper develops a mathematical model for the production planning decision of a dairy plant that determines the optimal product-mix, which maximizes the expected profit of a dairy under disruption risk and demand uncertainty (in the Indian context).

In the current global market, managing supply is not a straightforward process and it becomes even more complex as uncertainty and disruptions occur. In order to mitigate their impact, the selectio...

International audience; The contribution of this paper is threefold: first, it introduces a new geopolitical risk index that incorporates recent geopolitical events ignored in Caldara and Iacoviello (2018)’s index. In addition to war threats and acts, terrorist threats and acts and nuclear threats, the new indicator accounts for global trade tensions, the changing fundamentals of U.S.-China relations, the escalated U.S.-Iran conflict, Saudi Arabia’s uncertainty, Venezuela’s crisis, and OPEC news that rise in response to important OPEC meetings and events connected with OPEC production levels. Second, it addresses how the volatility of six oil prices (the Nigerian Bonny Light, Brent, Dubai, OPEC, Tapis, and WTI) behave when the developed geopolitical risk index unexpectedly changes. Third, it examines whether OPEC maintains a buffer of spare capacity that it uses to respond to potential crises that reduce oil supplies. We show that an increase in the geopolitical risk index is significantly associated with unanticipated oil price changes, though with varying sensitivities. Our findings also reveal that OPEC’s use of spare capacity reduces the reaction of oil price to geopolitical risks but moderately, thus suggesting a limited stabilizing influence on the oil market. The limited amount of spare production capacity leaves the oil market on a knife’s edge as it deals with a host of potential supply disruptions stemming from geopolitical issues.

Load shedding (LS) is implemented by distribution utilities (DUs) in addressing power supply insufficiency problems to avoid DU system damages. This is commonly implemented by the installation of LS relays in every DU feeder. However, in either scheduled or unscheduled supply disruptions, a huge amount of unnecessary de-loading is taking place in a feeder level LS implementation. In addition, the consumers connected to a de-loaded feeder are in total blackout, that is, consumers have no choice over which appliances to spare from being de-loaded. This study proposes an LS implementation that replaces feeder-level de-loading by a finer consumer-appliance-level de-loading and allows consumers to have some control over their de-loading. In this method, consumers can set an appliance priority level to their selected connected loads at a given time, to avoid a total blackout. Furthermore, to deal with the enormous data involved in this proposed method, both centralised and distributed optimisation approaches are employed to expedite the system processing response. Simulations are conducted to verify the proposed method's functionality. Lastly, economic analysis is done to assess the proposed method's viability.

The spread of COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. The devastating effect on global automotive supply chains due to the disruptions has resulted in losses and shut down of production facilities owing to government restrictions due to the spread of infections leading to supply breakdown across all tiers of supply chain. It has resulted in shortage of crucial components to the automobile industry. A stochastic goal programming model is developed to incorporate disruptions in the supply chain. The model is used to simulate the current crisis of COVID-19 as numerical cases. Supply chain resilient strategy such as China Plus One strategy that involves diversification of supplier base to other locations along with China is analysed. The key finding is that a China-Plus-One strategy appears to be beneficial to both parties involved, organizations that seek to pursue it for reasons of risk diversification, cost reduction or avoidance of overreliance on China and for Plus-One host economies which gain the benefits of FDI. The contribution of the study is in integrating the supply chain resilience literature and operational research tools to model a global supply chain under uncertainty and making a case for change in supply chain strategy from efficient supply chain to resilient supply chains. The study attempts to support the implementation of the China Plus One Strategy as the most suitable resilient strategy post COVID-19. Spridningen av covid-19-pandemin i världen och vikten av att kontrollera den i alla regioner har gjort att hantera denna kris till en stor utmaning för alla länder. Den förödande effekten på globala fordonsförsörjningskedjor på grund av störningarna har resulterat i förluster och nedläggningar av produktionsanläggningar på grund av statliga restriktioner på grund av spridningen av infektioner som leder till leveransuppdelning över alla nivåer av försörjningskedjan. Det har resulterat i brist på avgörande komponenter till bilindustrin. En stokastisk målprogrammeringsmodell utvecklas för att införliva störningar i försörjningskedjan. Modellen används för att simulera den nuvarande krisen med COVID-19 som numeriska fall. En strategi för motståndskraftig leveranskedja som China Plus En strategi som innebär diversifiering av leverantörsbasen till andra platser tillsammans med Kina analyseras. Nyckelresultatet är att en China-Plus-One-strategi verkar vara till nytta för båda inblandade parter, organisationer som försöker eftersträva den på grund av riskdiversifiering, kostnadsreduktion eller undvikande av övertilltro till Kina och för Plus-One-värdekonomier som vinner fördelarna med utländska direktinvesteringar. Studiens bidrag är att integrera litteraturen om försörjningskedjans motståndskraft och operativa forskningsverktyg för att modellera en global försörjningskedja under osäkerhet och göra ett argument för förändring av strategin för försörjningskedjan från effektiv försörjningskedja till spänstiga försörjningskedjor. Studien försöker stödja implementeringen av China Plus One-strategin som den mest lämpliga motståndskraftiga strategin efter COVID-19.

In this study, a multi-period contingent sourcing model for sustainable sourcing is developed to mitigate the ripple effect caused by supply disruption. In this model, the manufacturer's main suppl...

Enterprise captures market share from competitors through brand advertising and increases consumer demand for this category of products through general advertising. In this paper, considering a sup...

© 2019 The Authors. International Transactions in Operational Research © 2019 International Federation of Operational Research Societies We study a financing problem in a supply chain (SC) consisting of one supplier and one buyer under supply disruption. The supplier could face a disruption at its end which could effectively reduce its yield in case of disruption, thereby resulting in supply yield uncertainty. The retailer can finance the supplier using advance selling that can help mitigate the impact of disruption. We model this problem as a Stackelberg game, where the supplier as the leader announces the wholesale price and the retailer responds by deciding its optimal order quantity given stochastic demand and an exogenous fixed retail price. The supplier then commences production and a disruption can happen with a known probability. We assume that under disruption the quantity delivered is a fraction of the initial quantity ordered by the retailer. The retailer loses any unmet demand. We analyze three different scenarios of the Stackelberg game, namely no advance selling with disruption, advance selling without disruption, and advance selling with disruption. Our results indicate that advance selling can be used to mitigate the impact of supply disruption and at the same time could lead to an increase in the overall SCprofit.

When a disruption caused by human or environmental accident occurs in production systems, it may cause a shortage of the supply, and thus the buyers’ procurement behaviors will be influenced. This paper studies a supply chain comprised of a buyer and two types of suppliers: one is cheap but unreliable and the other is reliable but expensive. If there is a major disruption, the unreliable supplier may not be able to fully satisfy the buyer’s order, despite the fact that it exerts additional effort to rebuild capacity; at the same time, the reliable supplier cannot fulfill extra orders from the buyer due to capacity constraints. In this way, the buyer should strategically allocate its order between the two types of suppliers by offering different contracts at the very beginning, and then the unreliable supplier chooses its optimal restoration effort according to the contract if a disruption occurs. The model is built based on the real-life cases such as Walmart and Apple such that it is the buyer who determines the wholesale price of the unreliable supplier’s products. The results show the optimal contracts provided by the buyer under different circumstances, which aims to help managers design their contracts under disruption risks to maximize the company’s profit.

This article describes using a nonlinear APVIOBPCS to model the resilience response to the failure of one of the suppliers of a two-tier supply chain comprising a retailer and two suppliers. The second supplier then supplies all the goods required by the retailer. The model is chosen as the simplest case to examine resilience. Time to recover from disruption to ship all goods required by customers is slightly worse when the order rate when it reaches capacity limits but is less than the delay in the system from supplier to final shipment. Just over one weeks' maximum shipments stock at each tier guarantees shipments impervious to the collapse of one supplier, controller type has little effect on the performance of the model. These results agree with other researchers in general trend but not in detail. The response does not match the ‘Resilience Triangle.'

Catastrophic forest disturbances, such as wildfires, insect outbreaks, and hurricanes, have become more frequent in recent decades. Such disturbances can create supply disruptions in regional timber markets, with potentially significant short-run and long-run price effects. We review the time-series intervention models that have been used to analyze the impacts of forest disturbances. We apply the intervention models to investigate the market effects of the Biscuit Fire that burned nearly 500,000 acres (202,000 hectares) of forest land in southwest Oregon in 2002, thus creating an unexpected supply shock in the regional timber markets. Most of the burned area was located on federal lands. Although almost two billion board feet were available for harvesting by some estimates, the salvage logging on public lands after the Biscuit Fire amounted to 60 million board feet by the end of 2005. We use a univariate and reduced-form model to estimate the effect of the Biscuit Fire on regional Douglas-fir log markets. We find that the fire did not cause immediate price effects; however, we detect positive effects during the salvage logging period in some of the markets, whereas a negative long-run effect has persisted since the salvage logging period was completed.

In this paper, a mixed-integer linear programming model is discussed to provide joint decision making for facility location and production–distribution across countries for both forward and reverse logistics. A hybrid facility network is considered for cost-cutting and equipment sharing where the facilities of forward logistics are also equipped to provide reverse logistics services. The model considers the dynamic production and storage capacity of the facilities which can be expanded if required. Furthermore, the effectiveness of the model is tested to deal with disruptions due to man-made or natural disasters. The dynamic facility allocation enables the model to withstand the demand/supply disruptions in a disaster-affected zone. Besides this, the model considers carbon emissions caused due to manufacturing, remanufacturing, repair, storage and transportation. These emissions are regulated using cap and trade policy Thus, the proposed model balances resilience and sustainability under uncertain market demand and product returns. The chance-constrained approach is used to obtain the deterministic equivalence of the stochastic demand and returns. The paper also investigates the changes in emission and production level in each country under demand and supply disruptions. The parameters of the model are mapped with the various dimensions of big data such as volume, velocity and variety. The proposed model is solved using randomly generated data sets having realistic parameters with essential big data characteristics.

Rare earth elements (REEs) are important high-tech materials with widespread industrial applications and a high risk of supply disruption. Current physico-chemical methods for REE recovery are energy intensive, complex, and costly. Thus, it is crucial to undertake measures to secure future REE demand and protect the environment as well. Bioleaching as an alternative technology for the recovery and recycling of REEs from a variety of primary and secondary sources has been increasingly explored. The present review provides an overview on REE content of various primary and secondary sources, applied bioleaching microorganisms and methods, reactor setups, as well as a detailed description of known REE leaching mechanisms. The effects of different process parameters including temperature, pH, medium composition, pulp density, and particle size on efficiency of REE leaching are also evaluated in detail. acceptedVersion

This paper studies multi-tier supply chain networks in the presence of disruption risk. Firms decide how to source their inputs from upstream suppliers so as to maximize their expected profits, and prices of intermediate goods are set so that markets clear. We provide an explicit characterization of equilibrium prices and profits, which allows us to derive insights on how the network structure, i.e., the number of firms in each tier, production costs, and disruption risk affect firms' profits. We discuss the prescriptive implications of our findings by exploring how a firm should prioritize among its suppliers (direct and indirect) when investing in improving their production reliability. Furthermore, we establish that networks that maximize profits for firms that operate in different stages of the production process, i.e., for the upstream supplier and the downstream retailer, are structurally different. In particular, the latter have relatively less diversified downstream tiers and generate more variable output than the former. Finally, we study the question of endogenous chain formation by considering a game of entry, i.e., firms decide whether to engage in production by forming beliefs about their profits in the post-entry supply chain. We argue that endogenous entry leads to chains that are inefficient in terms of the number of firms that engage in production.

Giant retailers sell their store brands and (re)sell substitute national brands for manufacturers (suppliers) simultaneously, which becomes an important sourcing and selling supply chain strategy i...

We consider a supply chain consisting of a supplier and a buyer. The buyer faces demand as a function of the selling price. There is risk of supply disruption, but the supplier can rebuild his capacity if he has invested for the capacity restoration before a disruption. To motivate the supplier to invest, the buyer can use one of two incentive contracts, namely direct and indirect . With a direct contract, the buyer provides a financial subsidy to share the supplier's capacity restoration cost when disruption occurs. With an indirect contract, the buyer adjusts the wholesale price in the case of disruption to stimulate the capacity restoration. Both contracts allow the buyer to adjust the order quantity in the case of disruption. We analyze decisions of each supply chain member under the two contracts with different commitment strategies (the ex ante commitment (EA) strategy under which the contract is signed before disruption and the ex post commitment (EP) strategy under which the contract is signed after disruption), and study how the supply disruption affects the buyer's selling price to end customers. We show that, from the perspective of either the supplier or the buyer, the incentive contract under the EA strategy is preferred by leading to a greater profit. Further, through numerical experiments, we recognize conditions for the fixed investment cost, probability of disruption, and regular wholesale price, under which each incentive contract encourages the supplier to invest for the capacity restoration, and brings greater profit to each supply chain member.

The trade dispute between the US and China has reached a point where export restrictions on rare earths have become a possible means for sanction. This contribution provides a succinct analysis of the current dependency of the US on rare earths from China and draws parallels to the events during the rare earth crisis of 2010 and 2011, which was caused by geopolitical tensions between Japan and China. Respective risks associated with restrictions on the trade of rare earths are considered. Findings suggest that Europe could be affected as well and should be better prepared to mitigate supply disruptions in rare earth trade. Potential mitigation strategies and consequences of the current developments for Europe are also discussed.

Due to the prevalence of supply disruptions disturbing supply chain and logistics operations, a firm should tackle such disruptions using appropriate approaches. In this research work, a dual-sourcing approach is applied by a firm. The main decision regarding the firm sourcing from two unreliable suppliers is to derive an optimal ordering policy that maximizes the firm’s expected profit. Considering the stochastic ordering yield bridges a gap in the literature. The firm’s expected profit function is analytically formulated, and the viability of the developed mathematical expression is illustrated using numerical experiments and sensitivity analyses. Through the sensitivity analyses, the importance of considering the stochastic ordering yield is confirmed, as the optimal ordering policy varies on the variance of the ordering yield. Furthermore, failing to consider the stochastic ordering yield leads to incorrect conclusions regarding the optimal ordering policy; in addition, the increase in the variance of the ordering yield prevents the firm from benefiting from incremental profit.

This paper studies a continuous-review stochastic inventory problem for a firm facing random demand and random supply disruptions. The supplier experiences operational (on) and disrupted (off) periods with exponentially distributed durations. The firm adopts an order-up-to level policy during the on period and additionally can release an emergency order based on the inventory level just before disruption. This inventory policy is described by a continuous-time Markov chain model. We analyze the model for two different lead time scenarios and suggest solution approaches yielding the optimal policy parameters. In a numerical study, we explore the value of exercising such a policy and show that an emergency ordering opportunity at the disruption time brings substantial cost savings in cases with high lost sales cost, long off period, and low percentage of supplier’s availability.

Russia saw strong growth in 2021, with momentum weakening late in the year. Growth will slow as Russia battles COVID-19 and elevated inflation. Globally, environmental sustainability is becoming central to the economic agenda. The challenge for Russia is to fundamentally transform its economic structure.

Recent supply disruptions catapulted the issue of risk in global supply chains (GSCs) to the top of policy agendas and created the impression that shortages would have been less severe if GSCs were either shorter and more domestic, or more diversified. But is this right? We start our answer by reviewing studies that look at risks to and from GSCs, and how GSCs have recovered from past shocks. We then look at whether GSCs are too risky—starting with business research on how firms approach the cost-resiliency trade-off. We propose the risk-versus-reward framework from portfolio theory as a good way to evaluate whether anti-risk policy is justified. We then discuss how exposures to foreign shocks are measured and argue that exposure is higher than direct indicators imply. Finally, we consider the future of GSCs in the light of current policy proposals and advancing technology before pointing to the rich menu of topics for future research on the risk-GSC nexus.

The COVID-19 pandemic crisis marks a rare example of a supply disruption that had a devastating effect on the global economy. The vaccine has then been considered to be an effective long-term solution. The particularity of the supply chain of the COVID-19 vaccine is that the procurement is made by government agencies directly from the manufacturers, thus bypassing the typical vaccine supply chain of distributors and wholesalers. The first purpose of this paper is to further understand the public procurement strategies (PPSs) for the COVID-19 vaccine in a pandemic situation, using a methodology that brings together Kraljic’s purchasing portfolio model, Porter’s five forces analysis and a multi-attribute decision making method. The second aim is to evaluate the sustainability of a PPS, using the sustainable development analytical grid (SDAG), an analytical tool that addresses six dimensions: ecological, social, economic, ethical, cultural and governance. To assess the effectiveness of the methodology, we consider Morocco, an emergent country, as a case study. It results that Morocco’s approach is in line with the theoretical strategy: diversification and bringing the production in house. The sustainability assessment shows that the PPS covers the six dimensions, but considering the urge for an economic and social recovery, some sustainable development objectives are to be prioritized.

Supply risk assessments are an integral part of raw material criticality assessments frequently used at the country or company level to identify raw materials of concern. However, the indicators used in supply risk assessments to estimate the likelihood of supply disruptions vary substantially. Here, we summarize and evaluate the use of supply risk indicators and their normalization to supply risk scores in 88 methods published until 2020. In total, we find 618 individual applications of supply risk criteria with 98 unique criteria belonging to one of ten indicator categories. The most often used categories of supply risk indicators are concentration, scarcity, and political instability. The most frequently used criteria are the country concentration of production, depletion time of reserves, and geopolitical risk. Indicator measurements and normalizations vary substantially between different methods for the same criterion. Our results can be used for future raw material criticality assessments to screen for suitable supply risk indicators and generally accepted indicator normalizations. We also find a further need for stronger empirical evidence of widely used indicators.